A Hierarchy of Near-Optimal Policies for Multistage Adaptive Optimization

Multistage decisions under uncertainty are usually attacked with simple policy classes, because the exact problem is intractable. We introduce a hierarchy of polynomial disturbance-feedback policies, each computable from a single semidefinite program and indexed by the polynomial’s degree. Raising the degree buys accuracy at a predictable computational price.

IEEE Transactions on Automatic Control, vol. 56, no. 12, pp. 2809–2824